Project 2 Multiscale spatial and temporal dynamics of yeast colony development Introduction. In living systems, the characteristics of an individual, including traits such as susceptibility to disease or response to therapy, are determined by the coupling of processes that function at different scales of organization. For example, an individual's DNA sequence constrains the molecular networks that govern its cellular states and behaviors, which in turn determine the form and functions of multi-cellular structures. Microorganisms, including the yeast Saccharomyces cerevisiae, are traditionally used as models for investigating basic cellular processes at the unicellular level. However, unicellular organisms can form multi-cellular communities and differentiate into specialized structures to benefit the population. In some wild isolates of S. cerevisiae colonies (which start from a single cell and divide mitotically to become a structure of -10[8] cells) undergo a morphological transition characterized by complex patterns of wrinkles on the colony surface (Fig. 11). This trait is called the fluffy phenotype. Work by others has shown that fluffy yeast colonies possess many properties of microbial biofilms and are thus directly relevant to health and human disease. In fluffy colonies, cells are connected by an extracellular matrix and internal hollow channels, which may help exchange nutrients and waste products. While there is some evidence that this morphological development involves nutrient driven cell state transitions, cell-cell signaling, quorum sensing and cell death, the exact molecules and in many cases the pathways are largely unknown. The importance of the experimental system: This project seeks to understand how cells establish and maintain spatiotemporal patterns of cell state transitions to form multicellular structures. In our model (yeast fluffy colony formation) a single cell divides a undergoes a series of metabolic and functional transitions to reproducibly self organize into a complex structure of 10[8] cells. By advancing the conceptual, computational, and technical challeges below, we will develop a general methodology for analyzing complex traits that exhibit morphological phenotypes and can thus be applied to problems as diverse as physical birth defects during development or angiogenesis during tumor growth. The challenges: Conceptual challenges: Among the most fundamental problems in biology is the genotype-phenotype question: given the complete genome sequence of an individual, can we predict the traits that the individual will exhibit? The newly emerging field of systems genetics seeks to solve the genotype phenotype problem by applying the principles and technologies of systems biology to genetic analysis. A major limitation to this approach is that while the genotype side of the equation is data-rich (e.g., billions of nucleotides), traits are defined by an extremely limited set of values (e.g., a blood glucose level or age of disease onset). Recent methods, including new machine learning algorithms that we have helped develop, have attempted to overcome this data sparsity problem by using comprehensive (omic) data, such as RNA expression levels. Unfortunately, such data are most informative about the molecular networks (to which they are more proximal) and many steps removed from the traits of interest, which can result from processes that span multiple biological scales (molecular networks, cells, tissues, and organs). To advance systems genetics in a way that is less confounded by this problem, we have chosen an organism and a trait, colony morphology in yeast, where imaging colony structure and spatial patterns of gene expression can be used to extract an extremely rich set of parameters that are directly relevant to the trait being studied.